Similitude Based Segment Graph Construction and Segment Ranking for Automatic Summarization of Text Document
DOI:
https://doi.org/10.48048/tis.2022.1719Keywords:
Single document summarization, Graph-based summarization, Graph scoring, Sentence segment scoring, Information gain ratioAbstract
With the increase in the amount of data and documents on the web, text summarization has become one of the significant fields which cannot be avoided in today’s digital era. Automatic text summarization provides a quick summary to the user based on the information presented in the text documents. This paper presents the automated single document summarization by constructing similitude graphs from the extracted text segments. On extracting the text segments, the feature values are computed for all the segments by comparing them with the title and the entire document and by computing segment significance using the information gain ratio. Based on the computed features, the similarity between the segments is evaluated to construct the graph in which the vertices are the segments and the edges specify the similarity between them. The segments are ranked for including them in the extractive summary by computing the graph score and the sentence segment score. The experimental analysis has been performed using ROUGE metrics and the results are analyzed for the proposed model. The proposed model has been compared with the various existing models using 4 different datasets in which the proposed model acquired top 2 positions with the average rank computed on various metrics such as precision, recall, F-score.
HIGHLIGHTS
- Paper presents the automated single document summarization by constructing similitude graphs from the extracted text segments
- It utilizes information gain ratio, graph construction, graph score and the sentence segment score computation
- Results analysis has been performed using ROUGE metrics with 4 popular datasets in the document summarization domain
- The model acquired top 2 positions with the average rank computed on various metrics such as precision, recall, F-score
GRAPHICAL ABSTRACT
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